cellshighlights

cellshighlights is a term used in computational biology to describe methods and tools designed to emphasize and annotate specific cells within high-dimensional cellular datasets. This approach aims to improve the interpretability of complex data by visually distinguishing subsets of cells based on gene expression, metadata, or spatial location.

Core ideas include selecting cells by predefined criteria and applying color-coded overlays or markers to plots such as UMAP or t-SNE, as well as providing linked views (violin plots, heatmaps) to summarize the highlighted populations. Implementations often appear as library features, web apps, or notebook workflows that integrate with common single-cell analysis pipelines.

Selection criteria can be threshold-based (for example, expression of particular marker genes above a cutoff), cluster-based, or metadata-driven (such as tissue origin or experimental condition). The highlighting is typically interactive, enabling researchers to refine the criteria and immediately see the effects on visualizations and summaries.

Typical workflows involve preprocessing to normalize and reduce dimensionality, followed by defining highlight rules and generating multi-view figures that include highlighted plots along with supporting statistics. Outputs commonly include annotated UMAP or t-SNE plots, feature overlays, and side panels showing expression distributions or metadata for the highlighted cells.

Applications of cellshighlights include exploratory data analysis, discovery of rare or transitional cell states, and communication of findings in publications or presentations. Limitations include reliance on user-defined criteria and preprocessing choices, potential biases from normalization and batch effects, and the risk of overcrowding visuals when many cell groups are highlighted.

See also: single-cell analysis, dimensionality reduction, UMAP, t-SNE, data visualization.